The party list for the Knesset is chosen by the Shura Council of the southern faction in the Islamic Movement. The party enjoys great sympathy among the Bedouin public, and is considered the most pragmatic of the Arab parties in relation to cooperation with Jewish Zionist and even nationalist parties. Meanwhile, the party sat in the previous coalition under Naftali Bennett's Right Party and then under Yesh Atid's Yesh Atid.
The data files we used for the report:
In the background of the last three election sets, in our opinion it is vital to note the entry of Mansour Abbas into the position of the chairman of the party in April 2019 under the name Ra'am - Balad.
Meanwhile, running for the Knesset as part of the 2020 elections (the 23rd Knesset), the Ra'am party was registered as part of the joint list and won 4 mandates out of 15 of the entire list.
However, during the election campaign for the twenty-fourth Knesset (March 2021), Chairman Mansour Abbas decided to lead the party to withdraw from the joint list and run independently.
After that, we cross-referenced the location data of the localities with the voting data for the party in each locality and created the heat map.
From the analysis of the heat map (interactive map) it is evident that most of the voters for Raam come from the Israeli periphery, from Arab and Bedouin communities.
This increase is also reflected in the number of mandates the party received in the last elections (5) compared to the previous elections (4).
However, it is important to note that the fact that Balad and Meretz parties did not pass the percentage of blocking in the 2022 elections compared to 2021, had a significant effect on Ra'am's "growth" in the number of mandates, as we will see in the following sections of the report concerning vote transfers between parties .
After all, these three parties belong to the left bloc, while the Balad and Ra'am parties have a Gordian connection and are considered to be two of the three almost exclusive parties that represent the Arab-Israeli voters.
(<Figure size 640x480 with 1 Axes>,
<AxesSubplot: title={'center': 'Votes percent םער party 2022'}, xlabel='םער', ylabel='Votes percent'>)
(<Figure size 640x480 with 1 Axes>,
<AxesSubplot: title={'center': 'Votes percent םער party 2021'}, xlabel='םער', ylabel='Votes percent'>)
Therefore, the party should and can concentrate its efforts in trying to "bite" a little into this gap and enter the vacuum.
This is because the party still has much room to grow from the potential (more than 20%).
In addition, we will demonstrate analyzes in comparison to the other Arab parties in Israeli politics and we will demonstrate this through the support from among cities characterized as Jewish.
Finally, we will refer to the economic-social analysis and we will see the segmentation of the frequency of voting for the רעם party in the division into social economic clusters and we will try to diagnose a relationship between the percentage of voters for the party from a certain locality and the Gini index for inequality.
It is understood that this is a rather simplistic analysis, but in our opinion it was important to start from it in order to illustrate which localities have the highest absolute number of support for the party in order to show its centers of power in a quantitative - absolute sense.
Clearly, it is possible to distinguish the significant differences in the support received by the Ream party among smaller cities that are homogeneous in their religion and affiliation. This is in contrast to more "developed", mixed, and large cities, where the other parties receive significantly wider support.
Our message for רעם in this case is - At the same time as maintaining power in the small and homogenous localities, you must invest in advocacy and communication efforts in the mixed and larger cities in order to expand your support camp and "bite" into the audience of voters from the Arab sector who currently vote for other parties
Although in some of them the rate of relative prevalence is not low, it is evident that the party has quite a bit of room to grow into it and increase the rate of support in these localities, certainly based on the previous analyzes that we presented in front of the other Arab parties.
However, we must not ignore the fact that there is some support base for the party, which needs to be developed and expanded with different and adapted tools.
Also, it can be noticed that while the percentage of support for רעם is relatively high in the Jewish peripheral cities (north and south), in the larger central cities such as Ramat Gan and Givatayim, רעם is pushed to the margins.
We can interpret the primary components in the following manner:
It can be inferred that in the lower-right area of the graph, the party relatively gained more votes in areas considered more conservative ideologically and lost votes in more moderate areas. Additionally, the following mentioned villages, at least 25% of the votes were lost in at least one ballot compared to the previous year, and therefore it is recommended to focus on them in the next campaign: 'דייר חנא' 'לוד' 'מגאר' 'משהד' 'סחנין' 'עטאוונה שבט' 'רמלה'.
The advantage of PCA is its ability to effectively reduce the dimensionality of complex data while preserving its most important features, making it easy to visualize and understand. However, one disadvantage is that it can be sensitive to outliers and may not always produce the most accurate results. Alternative methods for data visualization and analysis include t-SNE, MDS, and LLE. The results obtained from PCA analysis are considered credible wheb the data is preprocessed and cleaned properly and the assumptions of the PCA are met.
Each "flow" indicates a pattern of voting between the two elections. Hovering over these flows pop up a text describing the specific change in voting – how much from the voters who voted to party j in 2021 voted to a certain party in the election of 2022.
So the numbers refer to the percentage of voters who share the same pattern of voting – voting to one party X in 2021 and voting to party Y in the election of 2022. If a party managed to keep all voters from elections 2021 in 2022, it will result in a flow equals 100% (1 in the plot).
We omitted every voting trend (a flow in the plot linking between a party in 2021 to a party in 2022) that is smaller than 0.5% - that seemed to be a good threshold as 0.5% out of the total number of 'רעם' voters only sum up to less than 1000 people (out of ~167,000-194,000) in both elections and we want to focus on the main, important trends.
The first describes how 'רעם' voters in 2021 voted in 2022, in other words – the distribution of 'רעם' voters in 2021. The plot indicates that 89% of 'רעם' voters in 2021 remained 'רעם' voters also in 2022. 8.1% percent of the voters in 2021 have decided to vote to 'בלד' in 2022, and 3.2% voted in 2022 to 'חדש תעל'. In total that accumulates to around 18,900 votes. Besides that, the party didn't lose any votes (that are summing up to a larger number than half percent of the party's voters).
| 22_עבודה | 22_הבית היהודי | 22_יהדות התורה | 22_בלד | 22_חדש תעל | 22_ציונות דתית | 22_המחנה הממלכתי | 22_ישראל ביתנו | 22_ליכוד | 22_מרצ | 22_רעם | 22_יש עתיד | 22_שס | 22_לא הצביעו | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 21_רעם | 0.0 | 0.0 | 0.0 | 0.080528 | 0.031526 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.887946 | 0.0 | 0.0 | 0.0 |
The estimated values obtained by this formula, which minimizes the regression problem as we saw in class: $\widehat{M} = [N^{(a)^{T}} N^{(a)}]^{-1}N^{(a)^{T}}N^{(b)} $
now, we want to check whether our model of predicting voting transfers is good in compare to other Parties. We will use bootstrap again, for the same reasons. Now we will split the data to 80% - train and 20% - test, which will be the data we will test how good is the prediction of our model, using MSE (describing distances between the real observations and the predicted values).
<AxesSubplot: >
| city | ballot | רעם | Difference from avg | Difference from prev year | |
|---|---|---|---|---|---|
| city_name | |||||
| רמלה | 8500 | 951.0 | 0.699605 | 0.640822 | 0.066474 |
| כאבול | 504 | 10.4 | 0.825776 | 0.517081 | 0.000000 |
| אעבלין | 529 | 13.0 | 0.694444 | 0.453260 | 0.211518 |
| חורה | 1303 | 14.0 | 0.927536 | 0.401710 | 0.000000 |
| סחנין | 7500 | 33.4 | 0.664179 | 0.373586 | 0.000000 |
| רהט | 1161 | 60.0 | 0.852368 | 0.371321 | 0.241678 |
| עכו | 7600 | 80.0 | 0.437736 | 0.357219 | 0.166363 |
| זרזיר | 975 | 9.0 | 0.909420 | 0.352246 | 0.452328 |
| שפרעם | 8800 | 43.0 | 0.549020 | 0.347466 | 0.064269 |
| תל שבע | 1054 | 16.3 | 0.943548 | 0.330056 | 0.058615 |
As a result, we want to look for yellow and large points, such as the point of "Zerzir", where the deviation from the average is 35% and the deviation from the previous year is more than 45%, which could indicate a suspicious ballot.
This approach allows for the identification of outliers, which may indicate potential fraud or manipulation. The advantages of this method include the simplicity of the calculation and the ability to identify specific ballots that deviate significantly from the norm. However, this method has some disadvantages as well. It assumes that the previous year's voting patterns are representative of the current year, which may not always be the case.
It also assumes that the average voting patterns in the city are representative of the overall population, which may not be true for smaller cities or for cities with diverse populations. Alternative methods for identifying suspicious ballots include statistical tests such as chi-squared tests. The results obtained from this method are credible, but a thorough investigation including other methods and evidence should be conducted to confirm or reject potential fraud or manipulation. Another method that can come to mind is calculating the MSE of each city, but since the fraud is happening in specific ballot and not city, it does not make sense to use MSE.
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